Skip to main content
Glama

ojp_parse_job_posting

Parse raw job posting text into a structured OJP template with field confidence ratings and a gaps list of missing information. Fill the skeleton to later validate the result.

Instructions

Produce a structured OJP extraction template from raw job posting text. Returns a document skeleton with EXTRACT* annotations, a fieldConfidence list (high/medium/low per field), and a gaps list of information commonly missing from postings. The calling agent should fill in the skeleton using its reasoning over the text, then call ojp_validate_job_posting to verify the result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesRaw job posting text (plain text, markdown, or extracted HTML text).
sourceUrlNoSource URL of the job posting, if known.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses that the tool returns a document skeleton with _EXTRACT_* annotations, a fieldConfidence list, and a gaps list. It doesn't mention destructive behavior or auth needs, which are not expected for a parse operation. The description is transparent about the output but could add safety details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (3 sentences) and front-loaded: first sentence states purpose, second lists outputs, third gives usage guidance. No unnecessary words, every sentence serves a clear function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple two-parameter schema and no output schema, the description covers the tool's function, output structure (fieldConfidence, gaps list), and workflow. It could mention error handling or input format constraints, but it is sufficiently complete for a straightforward extraction tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description adds minimal value beyond the schema. The description only paraphrases the 'text' parameter as 'raw job posting text', which is already in the schema. No additional constraints or format details are given.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool produces a structured OJP extraction template from raw job posting text, using specific verbs and nouns. It distinguishes from siblings by mentioning the follow-up call to ojp_validate_job_posting, which is explicitly named as a sibling tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use the tool (parse raw text) and what to do next (fill skeleton then call ojp_validate_job_posting). It also mentions the output structure, aiding the agent in using the tool correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/neogene-ai/otp-ojp-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server